Financial Data Decision Analysis

Course

URL study guide

https://studiegids.vu.nl/en/courses/2024-2025/E_FIN_FDDA

Course Objective

The purpose of this course is to provide students with the basic knowledge and skills required to do empirical research in finance, and to prepare students for the Research Project and the Master's Thesis. After completing the course, students should be able to:
- Work with financial data and perform regressions using the programming language R
- Construct a suitable regression model for testing a given hypothesis
- Critically evaluate the regression models used in research papers
- Correctly and critically interpret the results of regressions

Course Content

The course begins with an introduction to the programming language R, which you will use throughout the course to work with financial data and perform regressions. The main part of the course will then focus on linear regression, hypothesis testing, and what to do when the assumptions of the linear regression model do not hold. Finally, the last part of the course will focus on other regression techniques, including panel data methods, difference in differences, event studies, and discrete choice models. The course consists of a mixture of theory and application. While the theory is obviously important, it is equally important that you learn to apply the theory in practice. To facilitate this, both the weekly tutorials and the weekly quizzes will have a strong emphasis on working with data, running regressions, and interpreting regression results. This will help prepare you for the Research Project and the Master's Thesis.

Teaching Methods

The course will be taught in a hybrid manner. The main content of the course will be delivered in a series of prerecorded knowledge clips that students can watch (and rewatch) at their own convenience. The knowledge clips will be supplemented by a weekly in-person tutorial session, during which we will work through both practical and theoretical exercises related to the material from the knowledge clips. There will also be optional online Q&A sessions where students can ask any questions they may have related to the knowledge clips. Finally, there will also be in-person lectures on the first and last week, to introduce and conclude the course.

Method of Assessment

Weekly quizzes (50%) and a written exam (50%).

Recommended background knowledge

Students are expected to be familiar with the following undergraduate-level concepts from mathematics and statistics: standard deviation, variance, covariance, correlation, expected value, mean, median, natural logarithm, normal distribution. The first lecture of the course will include a brief review of these concepts.
Academic year1/09/2431/08/25
Course level6.00 EC

Language of Tuition

  • English

Study type

  • Master